Phase Alpha architects enterprise-grade agentic systems — from alignment layers and memory architecture to multi-agent orchestration and production governance.
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Long-running agents accumulate memory drift. Without TTL governance, rollback policies, and poisoning defenses, your agent's behavior degrades silently over weeks.
Guardrails bolted on post-deployment aren't alignment — they're liability. Constitutional constraints and behavioral boundaries need to be designed into the architecture from day one.
Multi-agent systems without sandboxed execution environments, signed tool artifacts, and explicit permission scoping are not production systems. They're demos with blast radius.
Regulators and boards now require traceable AI decisions. A system without OpenTelemetry spans, NIST AI RMF mapping, and human-in-the-loop audit trails is an institutional risk.
We treat Constitutional AI constraints, RLHF behavioral boundaries, and guardrail logic as first-class architectural primitives — not safety wrapping applied after the fact. Every agent we build has its alignment envelope specified before a single tool is wired.
We design across all three memory tiers: working memory for in-context reasoning, episodic memory for session persistence, and semantic/parametric memory for durable knowledge. Each tier has explicit formation, retrieval, and expiration policies — because unmanaged memory is an attack surface.
We don't retrofit governance. Every system we ship includes sandboxed execution environments, OWASP GenAI Top 10 mitigations, EU AI Act–aligned human oversight gates, and full OpenTelemetry observability from day one. If it can't survive a red team, it doesn't ship.
Puppeteer-subordinate architectures using LangGraph, AutoGen, and CrewAI. Specialist agents with scoped permissions, A2A protocol integration, and failure-isolated execution.
Constitutional constraints, behavioral red-teaming, prompt injection defenses, and runtime policy enforcement. Built in, not bolted on.
Episodic, semantic, and working memory tiers with vector store selection, RAG pipeline tuning, TTL governance, and poisoning-resilient retrieval.
Model Context Protocol–compliant tool ecosystems. Signed artifacts, ABAC-scoped permissions, short-lived credentials, and secrets management across tool chains.
Adversarial testing aligned to MITRE ATLAS and OWASP GenAI Top 10. Prompt injection, memory poisoning, tool misuse, and autonomous replication threat modeling.
Risk-adaptive HITL gates triggered by confidence thresholds, blast-radius estimates, and regulatory obligations under EU AI Act Article 14.
Full OpenTelemetry GenAI spans across prompts, tool calls, and safety filters. SIEM integration, SOAR automations, and board-ready audit trail generation.
Model-agnostic evaluation across Anthropic, OpenAI, Mistral, and open-source stacks. Domain-specific fine-tuning, MoE routing, and cost-performance optimization.
Model-agnostic by design. Stack selected to fit the problem, not the partnership.
A tier-1 bank's existing agent was generating false positives after 72-hour continuous runs — classic episodic memory drift. We replaced the flat context window with a three-tier memory architecture with signed episodic checkpoints and daily semantic consolidation. False positive rate dropped 84% in the first production month.
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A health system needed autonomous prior authorization agents that could satisfy both speed requirements and HIPAA audit obligations. We designed a puppeteer-subordinate architecture with risk-adaptive HITL gates, Firecracker-sandboxed tool execution, and full OpenTelemetry spans per CMS traceability guidelines. Time-to-authorization dropped from 4.2 days to 6 hours.
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The client had launched a customer agent with prompt-level guardrails that were being bypassed at a 12% rate in adversarial red team testing. We redesigned the alignment layer using constitutional constraints at training time, runtime policy enforcement via API gateway sidecars, and behavioral drift detection. Bypass rate in subsequent red-teaming: under 0.3%.
Read the architecture brief →We work with founders, engineering leaders, CTOs, and heads of AI at enterprises that have moved past experimentation.
Principal — Alignment Architecture
Former research engineer at a frontier AI lab, specializing in RLHF pipeline design and constitutional constraint systems. Led alignment architecture on three enterprise agents now in regulated production.
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Principal — Multi-Agent Systems
10 years in distributed systems before pivoting to agentic AI. Designed orchestration frameworks for two of the largest multi-agent deployments in financial services. Contributor to LangGraph core.
Published work →
When guardrails are designed in, not bolted on, the constraints reshape how you tier memory, how you scope tool permissions, and where you place your human-in-the-loop gates. The cascade is larger than most teams anticipate.
The 1,445% surge in multi-agent inquiries isn't hype — it's the same architectural inevitability that killed the monolith. Specialization, isolation, and composability are as relevant to agent design as they were to service design.
The winners in enterprise AI won't be the best talkers. They'll be the best at retrieval discipline, update governance, rollback, and audit trails. In 2026, memory policy is product strategy.
We map your architecture, surface the failure modes already present, and assess whether we're the right fit. No NDAs required to have a real conversation.
Resulting in a full architecture brief: current-state gaps, proposed system design, alignment envelope specification, and a phased engagement scope with fixed deliverables.
Adversarial red-teaming at each milestone, and weekly technical briefings. You own every decision and every artifact.
Governance documentation mapped to your regulatory obligations, a red team report, and a 90-day operational support window.
We take on a small number of engagements each quarter. Current availability: Q3 2026.
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